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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are using Azure Machine Learning Studio to perform feature engineering on a dataset.
You need to normalize values to produce a feature column grouped into bins.
Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Entropy MDL binning mode: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named <colname>quantized.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins
NEW QUESTION: 2
About the aperture, which of the following statements are wrong? (Multiple Choice)
A. F value expressing aperture size = diameter of lens aperture / focal length of lens
B. The aperture size is represented by f plus numbers, such as f4, f8, etc., the larger the number, the larger the amount of light transmission
C. Automatic aperture lens must be selected in places where outdoor light changes greatly
D. Excessive aperture leads to overexposure; too small aperture will result in underexposure
Answer: A,B
NEW QUESTION: 3
You are developing an Azure Function App by using Visual Studio. The app will process orders input by an Azure Web App. The web app places the order information into Azure Queue Storage.
You need to review the Azure Function App code shown below.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: No
ExpirationTime - The time that the message expires.
InsertionTime - The time that the message was added to the queue.
Box 2: Yes
maxDequeueCount - The number of times to try processing a message before moving it to the poison queue.
Default value is 5.
Box 3: Yes
When there are multiple queue messages waiting, the queue trigger retrieves a batch of messages and invokes function instances concurrently to process them. By default, the batch size is 16. When the number being processed gets down to 8, the runtime gets another batch and starts processing those messages. So the maximum number of concurrent messages being processed per function on one virtual machine (VM) is 24.
Box 4: Yes
References:
https://docs.microsoft.com/en-us/azure/azure-functions/functions-bindings-storage-queue